Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm

Using optimization algorithms and developing dispatch strategies are essential in sizing renewable energy systems to ensure optimal performance, cost-effectiveness, and sustainability. This study employs the Teaching Learning-based Optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The optimization results are validated using the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Furthermore, a novel dispatch strategy is developed to make an informed decision when using different energy sources. The strategy considers a 24-h foresight of upcoming electrical demand, solar irradiation, temperature, and wind speed. The developed dispatch strategy has led to a reduction in cost and excess electricity compared to the pre-prepared strategies. The energy sources employed include Photovoltaic panels (PV), Wind Turbines (WT), Diesel Generators (DG) with heat recovery capability, battery banks, and boilers to supply electrical and thermal demand. A Levelized cost of energy (LCOE) of 0.142 $/kWh is obtained for the PV/WT/DG/Battery/Boiler system. Although the three algorithms find almost similar optimal solutions, TLBO exhibits better convergence speed than PSO and GA. A comparison with HOMER software control strategies shows the developed dispatch strategy is 3.4% and 15.5% more efficient than Cycle Charging and Load Following strategies, respectively. Lastly, a comprehensive economic sensitivity analysis is performed to investigate the effect of inflation and discount rates on the size of components and final objective functions.

Automated summary

Using optimization algorithms and developing dispatch strategies are crucial for sizing renewable energy systems. This study employs the Teaching Learning-based Optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The developed dispatch strategy considers various factors and has led to cost reduction and improved efficiency.